E-commerce through online marketplaces is thriving.
When making a purchase online, several supporting vendors contribute to the transaction, such as a warehouse storing physical items for sale, a payment processor to collect and distribute funds, and a transporter for delivery of items. These entities often operate external to and independently of a marketplace, resulting in isolated processes and distributed information adhering to varying formats.
Peer-to-peer marketplaces are a segment of e-commerce in which nearly any individual with access to a networked computing device can become a seller in addition to a buyer, even for selling a single item. However, it is difficult for buyers to identify relevant items amongst the vast diversity of different sellers and items. Further, it is difficult for sellers to find specific users that are interested in their items.
Conventional systems require input by users to identify relevant items. Search terms query a database of products and return a results page of merchandise. Merchandise recommendation systems available from online shopping entities consider products primarily from the prism of the shopper by using shopping history, or to others shoppers, by using general shopping patterns (e.g., recommending products based on purchases by other shoppers viewing similar products). However, these recommendations are based strictly on cold, objective metrics without any consideration for social networking interactions between shoppers.
What is needed is a virtual shopping party technique that aims specific products at specific users during a specific time in a peer-to-peer marketplace.